* Kathleen McDill is a
Senior Financial Economist in the Division of Insurance and Research
of the Federal Deposit Insurance Corporation. Andrea Maechler is an Economist
in the Monetary and Financial Systems Department of the International Monetary
Fund.

We are very grateful to Philip
Bartholomew and Lynn Shibut for their insightful comments and helpful discussions.
The paper also benefited from valuable comments by Urs W. Birchler, Jürg Blum,
Hali Edison, Clemens Kool, Tonny Lybeck, and Philip Schellekens as well as
by participants in the 2003 Workshop on Banking and Finance organized jointly
by the Nederlandsche Bank and the Utrecht School of Economics. All errors
are our own. The findings, interpretations, and conclusions expressed in this
paper are entirely those of the authors and do not necessarily represent the
views or policy of the International Monetary Fund (IMF) or the Federal Deposit
Insurance Corporation (FDIC).

Abstract

This paper investigates the presence
of depositor discipline in the U.S. banking sector. We test whether depositors
penalize (discipline) banks for poor performance by withdrawing their uninsured
deposits. While focusing on the movements in uninsured deposits, we also account
for the possibility that banks may be forced to pay a risk premium in the
form of higher interest rates to induce depositors not to withdraw their uninsured
deposits.Our results support the existence of depositor discipline: a weak bank may not necessarily be able to stop a deposit
drain by raising its uninsured deposit interest rates.

I. INTRODUCTION

A. Motivation

Most countries deal with bank insolvencies on a relatively
regular basis.[1] Generally speaking, most countries
have their share of bad banks. If widespread, bank failures are typically very costly,
ranging anywhere from a few percentage points of GDP to as high as 50 percent
of GDP.[2] In an effort to minimize such costly
occurrences, policymakers and regulators have turned their attention increasingly
to market discipline as a tool to induce banks to conduct their business in
a transparent, effective, and sound manner.[3] In the banking sector, one form of
market discipline is depositor discipline—the ability of depositors to penalize
(discipline) banks for poor performance by withdrawing deposits. Investors
in bank liabilities, such as uninsured deposits or subordinated debt, actively
reward or punish banks for their relative performance. In the case of excessive
risk taking, depositors can demand higher yields on their liabilities or withdraw
their funds. By making risk taking more costly for banks, depositor discipline
should curb banks’ incentives to take excessive risk and hence should contribute
to the stability of the financial system.

Depositor discipline strengthens the banking system by making
bank managers accountable for their performance and depositors responsible
for their investment choices. It helps reduce the likelihood that depositors
(or their insurers) will implicitly subsidize the risks taken by their banks
and hence mitigates moral hazard. But an extreme form of depositor discipline
can lead to bank runs, which, if widespread, carry costly consequences for
the economy as a whole.[4]
As a result, few countries have been willing to rely fully on depositor discipline
as an instrument to curb excessive risk-taking behavior on the part of banks,
at least not without offering some form of deposit protection.[5] In the United States, the Federal
Deposit Insurance Corporation (FDIC) guarantees all deposits up to $100,000
per depositor. Deposit insurance, however, weakens depositors’ monitoring
incentives and undermines depositor discipline.[6] Demirgürç-Kunt and
Detragiache (2002), for example, provide evidence that explicit deposit insurance
tends to increase the likelihood of banking crises in a sample of 61 countries
over the years 1980–1997.[7]

In this paper, we investigate
the presence of depositor discipline in the U.S. banking sector. The main
hypothesis is that depositors withdraw uninsured deposits when bank performance
deteriorates. Using panel data on bank-specific information, we test whether
depositor discipline (or withdrawal of deposits) is linked to bank performance.
We also account for the dynamic relationship between the price and quantity
of uninsured deposits by controlling for banks’ ability to increase interest
rates when facing a possible deposit drain. Our goal is to capture
the dynamic interactions between price (interest rates) and quantity movements
in uninsured deposits.

When the quality of a bank’s fundamentals changes, both the
demand and supply of uninsured deposits may be shifting simultaneously, obscuring
the effect of depositor discipline. When bank fundamentals deteriorate, depositors
withdraw their uninsured deposits, shifting the supply curve of uninsured
deposits upward. As a result, the equilibrium moves upward along the bank’s
demand curve, raising the price of deposits and lowering their quantity. In
response, banks may offer a higher deposit interest rate to induce depositors
not to withdraw their uninsured deposits. This would shift the demand curve
upward, raising both the price and quantity of uninsured deposits. In the
new equilibrium, the price of uninsured deposits is higher, with an ambiguous
effect on the quantity of uninsured deposits. Clearly, depositors’ reaction
and banks’ response is a jointly determined process.

To account for these dynamic processes, we use the generalized-method-of-moments
(GMM) estimators developed by Arellano and Bond (1991) for dynamic panel data.
This generation of GMM models has the marked advantage that it is specifically
designed to handle autoregressive properties in the dependent variable (uninsured
deposits) and endogeneity issues between the dependent variable and an explanatory
variable (the interest rate of uninsured deposits).

Overall, our results support our choice of modeling interest
rates as an endogenous mechanism. We find that banks can raise their level
of uninsured deposits (as a share of total deposits) by raising their interest
rates, although they can do so only at a relatively high price. We also find
that, for a given increase in the interest rate of uninsured deposits, good
banks attract relatively more uninsured deposits than the average bank, whereas
bad banks may not necessarily be able to raise uninsured deposits.

Note that while our empirical evidence supports the existence
of depositor discipline (depositors recognize and penalize a bank’s bad performance),
our results do not allow us to say anything about the effectiveness of depositor
discipline (that is, the degree to which depositor discipline manages to reduce
bank managers’ risk tolerance). This limitation, however, is a common feature
of research in this area. We will return to this issue in Section I.C.

B. Deposit Structure in the U.S. Banking Sector

Deposits, including uninsured deposits, play an important
role as a funding source for banks. In 2002, total deposits represented over
65 percent of U.S. banks’ total assets.[8]
This is shown in Figure 1. Although the share of total deposits to total assets
has been falling progressively since 1992, this trend is primarily due to
the substitution of insured deposits for non-deposit liabilities. The share
of uninsured deposits to total assets, on the other hand, has been increasing
sharply since 1995, rising from 11 percent in 1995 to 16 percent in 2002.

Figure 1. Composition of Deposits
(as Share of Total Assets)

Despite the relatively large deposit insurance coverage,
only 12.2 percent of all U.S. banks (1,152 banks) have less than 5 percent
of their deposits that are uninsured; 22.3 percent of all banks (or 2,105
banks) hold more than 20 percent of uninsured deposits (as a share of total
deposits); and 4 percent of all banks (or 403 banks) have more than 40 percent
of their deposits in the form of uninsured deposits. At the far end of the
spectrum, 25 banks hold more than 90 percent of their deposits in the form
of uninsured deposits.[9] The distribution of
uninsured deposits (as a share of total deposits) across the U.S. banking
sector is illustrated in Figure 2.

Figure 2. Histogram of Uninsured Deposits, 2002

Most uninsured deposits are held by the largest banks in
the United States. Over 73 percent of uninsured deposits are held by the 172
banks that hold more than $5 billion in assets. The smallest banks (4,893
banks with less than $100 million assets) hold only 3 percent of total uninsured
deposits. The remaining 24 percent of uninsured deposits are held in 4,386
banks with assets between $100 million and $5 billion. The distribution of
uninsured deposits by bank size is shown in Figure 3, while the frequency
of banks by bank size is illustrated in Figure 4.

Figure 3. Uninsured Deposits (in U.S. dollars) by Bank Size

Figure 4. Number of U.S. Banks by Size Category

It is interesting to note that the size of the bank does
not significantly alter its reliance on uninsured deposits for funding. According
to Figure 5, small banks (with total assets up to $100 million) fund on average
13 percent of assets with uninsured deposits; banks with assets between $5
billion and $50 billion fund 16 percent of assets with uninsured deposits.
However, the difference between small and large banks is slightly larger if
we look at uninsured deposits as a share of total deposits because total deposits
represent a small funding source for larger banks. Small banks hold 16 percent
of uninsured deposits (as a share of total deposits), whereas large banks
(with assets between $5 billion and $50 billion) hold as much as 26 percent
of uninsured deposits (as a share of total deposits). Again, these figures
support the view that uninsured deposits represent an important funding source
for banks, regardless of size.

Figure 5. Balance Sheet (in U.S. dollars)

C. Related Literature

Researchers have expended considerable effort to measure
depositor discipline. The most widely used approach is the price-based approach,
which uses yield spreads (the difference between a market yield on bank debt
and a market yield on a risk-free asset such as government debt) as a proxy
for the market’s perception of bank risk. This strand of the literature investigates
whether depositors punish bad banks by demanding a higher yield spread for
holding uninsured bank liabilities. For example, Flannery and Sorescu (1996)
investigated
the nonlinear relationship between yield spreads on large uninsured bank liabilities
and various measures of risks and found that spreads were closely related
to balance-sheet and market measure of bank risk. Since then, the ability
of financial markets to discipline bank behavior by pricing banks’ uninsured
debt according to their risk has gathered significant attention (Evanoff and
Wall, 2001, Morgan and Stiroh, 2001, Hancock and Kwast, 2001, and Sironi,
2003).[10]

Another approach, the quantity-based approach, examines whether
depositors discipline their banks by withdrawing their uninsured deposits
whenever the performance of their banks is no longer satisfactory. For example,
Goldberg and Hudgins (1996, 2002) find that failed thrift institutions exhibit
declining proportions of uninsured debt before failure. Jordan (2000) finds
a similar result for New England banks in the 1990s.[11] Park and Peristiani (1998) examine
the effect of bank health on the
quantity of uninsured deposits and the interest rate on these deposits. They
find that riskier thrifts pay higher interest and attract smaller amounts
of uninsured deposits. More recently, McDill and Maechler (2003) note that uninsured depositors are more responsive
to movements in bank fundamentals in banks that already have low levels of
equity. Similar evidence of depositor discipline has been found in countries
other than the United States.[12]

From a slightly different perspective, Covitz, Hancock and
Kwast (2000) find that relatively weak banks are unwilling (or unable) to
issue subordinated debt in bad times. While Gilbert and Vaughan (2001) discern
no effect on the behavior of uninsured savings deposits following a public
enforcement announcement, Billet, Garfinkel, and O’Neal (1998) notice that
a downgrade from Moody’s increases a bank’s reliance on insured deposits.

This paper is based on the tradition of the quantity approach.
The purpose of the paper, however, is not to focus solely on how the quality
of bank fundamentals influences the behavior of uninsured deposits, a phenomenon
that is already well documented in the literature. Its objective is to add
a new dimension to the analysis and account for the fact that both prices
and quantities of uninsured deposits may change in response to deteriorating
fundamentals. When bank fundamentals deteriorate, depositors discipline their
banks by withdrawing their uninsured deposits. This raises the price of uninsured
deposits. Banks, however, can mitigate this drain on deposits by further raising
the deposit interest rates. Depending on depositors’ interest sensitivity,
troubled banks may continue to attract funds by offering higher interest rates
to compensate uninsured depositors for the increasing risk they bear. To our
knowledge, no paper has attempted to capture explicitly the possible endogeneity
between the quantity and price mechanisms of uninsured deposits.

In a general equilibrium framework, Boyd, Chang, and Smith
(2002) study the role of the elasticity of the deposit supply curve on banks’
behavior. In contrast to our model, their model focuses on the cost of funding
as banks’ binding constraint. In their model, when the supply of savings is
allowed to be interest sensitive, banks cannot costlessly adjust their deposit
interest rates in the face of higher deposit insurance risk premia and must
adjust their risk profile to reduce their funding costs. We are interested
in a similar phenomenon, except that our attention is on the quantity of uninsured
deposits as banks’ binding constraint. If deposits are an important source
of funding, and assuming that the supply of uninsured deposits is interest
sensitive, weak banks may not be able maintain their deposit base through
higher prices and presumably will need to reduce their risk profile.

Note that this paper does not shed light on the effectiveness
of depositor discipline in reducing banks’ risk appetite. It examines the
extent to which banks can (and do) raise their interest rates to retain their
deposit base. In a theoretical framework, Blum (2002) demonstrates that higher
prices may not necessarily ensure that a bank reduces its risk profile. Practical
limitations have constrained empirical studies to investigate how depositors
react to excessive bank risk taking, as opposed to how bank managers respond
to or prevent higher funding costs. In a recent empirical study on U.S. bank
holding companies, Bliss and Flannery (2002, page 361) conclude that the ability
of changes in security prices to influence subsequent managerial actions “remains,
for the moment, more a matter of faith than of empirical evidence.” In an
attempt to overcome this difficulty, Nier and Baumann (2003) examine the relationship
between bank asset risk and bank capital, and find that banks with a higher
share of uninsured liabilities choose larger capital buffers for a given level
of risk.

II. Modeling Approach

A. Empirical Methodology

We use the generalized-method-of-moments (GMM) estimators
developed for dynamic models of panel data by Arellano and Bond (1991).[13] This methodology, which will be referred
to hereafter as the Arellano and Bond (AB) model, is specifically designed
to address three econometric issues relevant to the present paper, namely
(i) the presence of unobserved individual effects (in the present case, bank-specific
effects); (ii) the autoregressive process in the data regarding the behavior
of uninsured deposits (i.e., the need to use a lagged-dependent-variable model);
and (iii) the likely endogeneity between the dependent variable, the quantity
of uninsured deposits, and one of the explanatory variables, the price (i.e.,
the interest rates) of uninsured deposits.

In this paper, we are particularly interested in capturing
the interaction between the movement in the quantity of uninsured deposits
and a change in these same deposits’ interest rate margin. The panel estimator
controls for this possible endogeneity by using internal instruments, that
is, instruments based on lagged values of the explanatory variables. We use
the following general reduced form:

(1)

where UDitis the share of uninsured deposits
to total deposits, Xit-1 is a vector of bank-specific variables,
IMit, the interest rate margin between the interest rate
on uninsured deposits and that on total deposits, and Mt
is a vector of macroeconomic variables.

The vector of bank-specific variables, Xit-1,
is included with a lag to account for the fact that balance-sheet and income-statement
information is available to the public with a certain delay. In some regressions,
we include an interest rate margin to control for movements in the price of
uninsured deposits. According to equation 1, a bank’s ratio of uninsured deposits
to total deposits, apart from bank-specific differences, is determined by
four main factors: previous behavior of uninsured deposits, movements of the
price variable, the evolution of the bank fundamentals, and general developments
in the macroeconomy.

B. Data

To investigate the presence of depositor discipline in the
United States, we use only public bank-specific information that is available
to depositors. These data are derived from data submitted quarterly by the
banks in the Quarterly Condition and Income Reports, generally known as the
Call Reports. The Thrift Financial Report (TFR) supplied the data for thrift
institutions. These data were supplemented with macroeconomic data to control
for factors that might influence broad movements in the availability of uninsured
deposits. The data for real GDP growth, GDP deflator, and inflation rate are
derived from the Bureau of Economic Analysis, whereas interest rate information,
such as the Treasury one-year constant maturities and the federal funds rate,
came from the Federal Reserve Board.

We initially collected data for all FDIC-insured institutions
from 1987 to 2000. We used an annual frequency and selected the June quarter’s
data, because June had the most complete reporting requirements for deposit
information. The initial data contained 19,852 FDIC-insured financial institutions
(banks and thrifts). First, we eliminated merged institutions at the time
of their merger. It was necessary to have a consistent approach to institutions
that had merged during the sample period. If we had used the standard merger
adjustment for banks, which involves creating a weighted average of the variables
of merged banks, a great deal of useful information about risky banks would
have been lost in the merger-adjusting process. Without any adjustments, the
levels of the bank-specific variables would have jumped each time two institutions
merged. Therefore, we decided to eliminate the merged institutions from the
sample at the time of their merger. This reduced the data set to 19,551 financial
institutions.

We also dropped those institutions for which we had fewer
than five years of observations, which reduced the number of institutions
to 12,921. For computational reasons, we removed the small banks, those that
had assets of less than $100 million (in 1996 dollars). This restriction further
decreased the sample to 3,095 institutions. The sample was further limited
to 1,863 because of missing data for explanatory variables.

In the regressions, all of the bank-specific variables are
lagged by one year in order to reduce the possibility of endogeneity in one
or more of the right-hand-side variables of the equation and because we think
depositors will normally react to bank-specific information after some time
has elapsed. This delay may stem from a delay in access to information or,
in the case of certificates of deposit (CDs), from a desire of the depositor
to avoid penalties by waiting until maturity to withdraw deposits.

C. Summary Statistics

The summary statistics are presented in Table 1. The names
of the variables are provided in column 1. The remaining three columns present
the summary statistics for the variables over the entire 1987–2000 sample
period (column 2), in 1989 (column 3), and in 2000 (column 4). For each period,
the table indicates the total number of observations and the average value
for each variable, with the standard deviation presented below in parentheses.
The number of banks included in the sample declines between 1989 and 2000,
both because the total number of U.S. banks fell over the sample period but
also because merged banks were dropped from the sample at the time of their
merger, as mentioned in the previous section.

Dependent variable

We define the dependent variable in the model as the
quantity of uninsured deposits as a percentage of total deposits, or UninsDep. According to Table 1,
uninsured deposits represented on average 15 percent of total deposits, rising
from under 14 percent in 1989 to over 19 percent in 2000 for the average bank
in our sample.

Price variables

To control for the dynamic process between the quantity of
uninsured deposits and their price, we compute UninsIntMarg, the margin between a bank’s interest rate on
uninsured deposits (Jumbo CDs) and its average deposit interest rate. This
price variable captures the desired composition of deposits in a particular
bank (i.e., whether a bank is willing to pay a higher price to attract more
insured or uninsured deposits). The interest rate data are inferred indirectly
from computing the ratio of interest expenditures on Jumbo CDs to the level of
total Jumbo CDs. A rise in UninsIntMarg
means that bank i pays a relatively
higher price for its uninsured deposits than for its insured deposits; hence,
the rise should be associated with a higher share of uninsured deposits to
total deposits, the dependent variable UninsDep.

To check the robustness of our results, we have computed
two alternative measures of interest margins: JumboIntMarg, the margin
between the interest rate of bank i on a jumbo CD and the industry
average interest on jumbo CDs; and DepIntMarg, the margin between the
deposit interest rate of bank i and the industry average deposit interest
rate. By capturing the price of deposits offered by a particular bank relative
to the price offered on average in other banks, these alternative price variables
help explain the allocation of deposits among banks (i.e., whether a particular
bank is willing to offer a higher interest rate relative to that offered by
other banks).

Bank-specific factors

The size of a bank may influence depositors’ willingness to
hold uninsured deposits at that institution. Larger banks may attract large
businesses that require large transaction accounts and a variety of special
lending and service arrangements. Larger banks may also be perceived as less
likely to fail, either because their larger, more diversified customer base
makes them appear more substantial (and safer) or because they may have a
larger chance of being bailed out if they should run into financial
difficulties. Against this background, we computed three proxies to control for
the size of the bank. First, the table of summary statistics presents the level
of assets (in thousands of 1996 U.S. dollars), or Assets. According to Table 1, the assets of an average bank in our
sample increased by over 60 percent between 1989 and 2000, increasing from $667
million in 1989 to $1,089 million in 2000. Because the constant (1996) dollar
value of Assets had such a large
relative variance when compared with the percentage of uninsured deposits to
total deposits, we included in our regressions the natural log of Assets, which we defined as our size
variable, Size. We also included
asset growth (AssetGrowth), defined
as the percentage growth rate of real assets between two years, to control for
differences between higher-growth banks and lower-growth banks. Over the sample
period, assets grew on average by 8.6 percent. Presumably banks with higher-than-average
growth are flourishing and may exhibit a higher demand for uninsured deposits. On
the other hand, high growth in a bank may indicate excessive risk taking and an
aggressive asset acquisition policy, possibly to the detriment of asset
quality, and thus be associated with lower uninsured deposits.

The remaining explanatory variables include a set of four
proxies for bank quality that uninsured depositors are most likely to monitor.
A bank’s equity level is an important indicator of the bank’s health and
ability to withstand adverse shocks. To examine the effect of equity on
uninsured deposits, we included the variable Equity, measured as the ratio of total equity capital (the residual
between liabilities and assets) to total assets, multiplied by 100 to convert
it into a percentage. According to Table 1, Equity
over the sample averaged 9 percent of assets and increased from 8.2 percent in
1989 to 10.5 percent in 2000. Another important indicator of bank health is
return on assets, RoA, which averaged
1.1 percent return over the whole sample, increasing from slightly less than
1.1 percent in 1989 to 1.3 percent in 2000.

Interest income and fees generated by lending activities,
which typically represent a major source of income for banks, affect overall
bank profitability and hence influence depositors’ willingness to keep uninsured
money in their banks. To capture this effect, we included loans and leases
as a percentage of assets, or Loans.
Loans represented slightly under 60 percent
of assets for the banks in the sample overall, increasing from 59 percent
in 1989 to 63 percent in 2000. We also included loans made for the purchase
of residential real estate as a percentage of total loans, or Residential.[14] These loans, which include only loans made for properties
that accommodate small numbers of families, are typically considered to be
very safe. Therefore, a bank that invests heavily in this type of loan (rather
than, for example, in commercial and industrial loans) would generally be
considered a low-risk bank. On average, the loan portfolios of the banks in
the sample consisted of 35 percent residential real estate loans; this percentage
rose from 31 percent to 40 percent between 1989 and 2000.

We also included a variable NonCLoans, which is
defined as the percentage of loans that are non-current — 90 days or more past
due — to capture the quality of banks’ loan portfolios. This variable is
typically considered to be a very good indicator of bank health. Over the
sample period, average NonCLoans fell 1.1 percentage points,
from 1.9 percent to 0.8 percent between 1989 and 2000, with a panel average of
1.7 percent across the sample.

Macroeconomic factors

To control for the general conditions of the banking system,
we included three macroeconomic variables to capture the possibility that
the behavior of uninsured deposits is linked to general economic conditions.
Our regressions contained the GDP growth rate and its lagged variable (GDPt and GDPt-1) to control for the general strength of the economy,
the change in the GDP deflator (Infl)
to control for the falling value of insurance limits,[15] and the one-year constant maturity U.S. Treasury bond interest
rate (Treas1). This Treasury interest rate controls
for the effect of changes in the return on alternative saving instruments
on bank deposits. We expect GDP
and Infl to have a positive relationship with
the dependent variable (UninsDep),
and Treas1 to have a negative relationship
with the dependent variable. The latter expectation is based on the idea that
bank deposits may lose their attractiveness when the return on other savings
instruments, such as Treasury bonds, rises.

Real GDP growth surged toward the end of the sample to 5.1
percent in 2000, whereas inflation remained fairly low throughout the period,
with a sample average of 2.7 percent. The one-year Treasury interest rate
fluctuated between 8.4 percent in 1989 and 6.2 percent in 2000.

D. Simple Correlations

Table 2 provides the partial correlations between the dependent
variable and bank-specific variables: in the top section, the partial correlation
is in levels; in the bottom section it is in differences.

The most noteworthy result is the contrast between the positive
partial correlation between the level of uninsured deposits and the interest
margin (top section) and the negative partial correlation between changes
in uninsured deposits and changes in the interest rate (bottom section). The
banks that exhibit a relatively high share of uninsured deposits are also
the banks that offer a relatively high interest rate on their uninsured deposits.
However, this does not mean that banks can necessarily raise funds from uninsured
depositors by raising their interest rates on uninsured deposits.

The negative relationship between a change in the price and
a change in the quantity of uninsured deposits suggests that some banks that
raise their interest rates experience a fall in the level of uninsured deposits.
This seems to indicate that some banks face an upward shift in their supply
of uninsured deposits. Although the data are marred with noise both across
banks and across economic cycles, some banks may have to offer a higher interest
rate in order to attract the same, or even a lower, level of uninsured deposits.

At this juncture in the model, a mechanism is needed that
allows us to differentiate between an upward shift in the supply curve of
uninsured deposits (where, for each given level of interest rate, depositors
are willing to supply a lower volume of uninsured deposits) and an upward
shift in the demand curve (where, for each given level of interest rate, banks
are demanding a larger quantity of uninsured deposits). Assume a bank’s deteriorating
fundamentals induce an outflow of uninsured deposits, which reduces the supply
of deposits and endogenously raises their price. The relationship between
price and quantity can be further obscured because a bank may respond to such
a deposit drain by offering a higher interest rate on its uninsured deposits.
Thus, it is important to use an econometric model that can control for the
endogenous price effect of a supply shift and can focus on banks’ independent
pricing decisions. Assume, for example, that a weak bank raises its interest
rate to retain its deposit base. At this higher price, some depositors may
be willing to leave their uninsured deposits with this (weak) bank. Even though
a simple reading of the quantity data may not necessarily suggest that depositors
penalize weak bank performance, the behavior of depositors would be consistent
with depositor discipline. These issues are examined empirically in the next
section.

III. Results

A. Controlling for Bank Fundamentals

As a first approximation, we examine the extent to which
movements in bank-specific variables explain the behavior of uninsured depositors,
controlling for general macroeconomic conditions. In this set of regressions,
we do not include any price mechanism. The dependent variable and the explanatory
variables are all expressed in first differences. The results are illustrated
in Table 3. Column 1 presents the results of the first-step estimation, while
column 2 presents the two-step estimates. Arellano and Bond (1991) recommend
using the first-step estimates for inference on coefficients, since these
are calculated using a robust estimator of the variance-covariance matrix.
The two-step estimates are used primarily to compute robustness tests.

The particular model specification (and variable choice)
presented in columns 1 and 2 has been selected because (i) it follows closely
the bank-specific variables used in earlier related papers, (ii) it replicates
standard results found in the literature using a minimum number of bank-specific
variables, and (iii) the results remain robust across a wide variety of variables
and groupings of variables.[16] Thus, we are confident that our regression results are not specific
to the particular choice of bank-specific variables included in our model.

The data seem to support our general hypothesis that the
fraction of uninsured deposits to total deposits is related to the movements
in a bank’s fundamentals. Except for non-current loans and leases, NonCLoans,
which is not statistically significant, we find that all of our bank-specific
variables are statistically significant and have the expected sign. For example,
a bank with a rising equity rate will experience an upward movement in its
share of uninsured deposits. This is shown by the statistically significant
and positive coefficient of Equity.

The coefficient of Size is positive and particularly
strong. Because the AB model uses all variables in first difference, the coefficient
for Size can be thought of as the effect of asset growth. It may also
reflect an increased demand for uninsured deposits on the part of the bank.
This possible demand effect is discussed in more depth below. Similarly, the
positive coefficient of ResidLoans is consistent with the relatively
safe nature of residential loans and suggests that uninsured depositors have
more confidence in a bank that exhibits a higher share of residential mortgage
lending in its credit portfolio.

The behavior of uninsured deposits seems to respond to business
conditions. All three macroeconomic variables are strongly significant with
the expected sign. The growth of uninsured deposits tends to rise during booms
(high GDP growth), during periods when inflation is increasing, and during
periods when Treasury bond rates are falling.[17]

It is interesting to note that our results suggest a strong
persistence in the behavior of uninsured depositors. If an innovation raises
uninsured deposits today, the effect of this innovation will persist over
the following year(s), although at a decreasing rate. This is shown in column
1 of Table 3, where the first lag and in some cases the second lag of uninsured
deposits (respectively, UninsDepL_D and UninsDep_L2D) are significant
in predicting next year’s growth of uninsured deposits. This result is confirmed
by the AB test of autocorrelation presented at the bottom of column 1.[18] The persistence in
the behavior of uninsured deposits reflects the relative stability of the
U.S. banking system. Typically, if a bank is small today, it will remain small
tomorrow and hence attract the same type of depositors. Similarly, if a bank
has a good reputation today, it is likely to enjoy a good reputation tomorrow.
But if something changes in the system — say, a bank’s fundamentals start
deteriorating — the results suggest that, in line with the depositor discipline
hypothesis, depositors will punish this bank by withdrawing their uninsured
deposits. Thus, persistence in the behavior of deposits can be consistent
with depositor discipline.

Finally, consistency of the GMM estimator depends also on
the validity of the instruments. To address this issue, we use the Sargan
test of over-identifying restrictions, which tests the overall validity of
the instruments. If the regression specification passes the test, we can safely
discard the possibility that the relationship between the quantity of uninsured
deposits (as a share of total deposits) and the price of uninsured deposits
is due to a simultaneity bias or omitted variables.[19] Unfortunately, the results for this regression do not satisfy
the Sargan test, suggesting that the model specification presented in columns
1 and 2 is misspecified. A possible reason for this misspecification may be
that we do not control properly for the dynamic interactions between the quantity
and price of uninsured deposits. This problem may be best illustrated with
an example.

We have seen above that the coefficient of Size is
positive and particularly strong, suggesting that depositors interpret strong
asset growth as a signal of good health. Presumably, however, strong asset
growth may also reflect a bank’s greater need for deposits, which may or may
not be a function of the bank’s good performance. For example, a bank that
engages in an aggressive asset acquisition policy may also experience strong
asset growth, although this would be the result of excessive risk taking rather
than prudent management. Assuming that this bank is willing to offer higher
interest rates to satisfy its growing funding needs, strong asset growth may
be associated with a rise in uninsured deposits, even if depositors recognize
the higher underlying risk profile of the bank. To properly conclude whether
good fundamentals or a bank’s desire to retain its uninsured deposit base
through higher interest rates causes a rise in uninsured deposits, we need
to control for movements in the interest rates. This issue is addressed in
the next section.

B. Introducing an Exogenous Price Mechanism

In this section, we examine whether the price (the interest
rate) of uninsured deposits influences the behavior of depositors. We include
UninsIntMarg, the margin between the interest rate on uninsured deposits
and that on total deposits, as an additional explanatory variable. In this
specification, we focus on whether an increase in the interest margin is associated
with a rise in the level of uninsured deposits (as a share of total deposits).

We find that an increase in the interest margin, when treated
as an exogenous change, has an effect on uninsured deposits that is very slightly
positive but not statistically different from zero. This is shown in column
3, where the coefficient of UninsIntMarg is positive but not statistically
significant. The positive relationship between changes in the price and quantity
of uninsured deposits contrasts with the negative partial correlation
shown in Table 2. By controlling for risk factors that shift the quantity
of uninsured deposits, we are able to capture some of the mechanisms driving
the negative partial correlation, although we are not yet able to say convincingly
whether banks can raise their level of uninsured deposits by raising the price
of uninsured deposits. The other bank-specific and macroeconomic coefficients
remain consistent with those found under the previous specification presented
in columns 1 and 2.

As in the previous section, our results are consistent with
the requirement of no second-order serial correlation and continue to fail
the Sargan test of over-identification. This suggests that we are still not
properly controlling for the dynamic process between the quantity and price
of uninsured deposits. One problem may be that we are missing a dimension
in the endogenous mechanism driving the behavior of uninsured deposits.

A bank’s decision to raise its interest rates may not be
independent of depositors’ desire to withdraw their funds, which in itself
is a function of banks’ risk profile. To address this issue, we need to control
for the impact of a change in bank fundamentals on the quantity and price
of uninsured deposits so that we can examine separately the fundamental effect
of a bank’s decision to raise its interest rates on the behavior of uninsured
deposits. If a bank’s deteriorating fundamentals cause depositors to withdraw
their uninsured deposits, the bank in response may decide to raise its interest
rate in an effort to retain its uninsured deposits. The overall effect on
uninsured deposits would be ambiguous, as suggested by our results. In practice,
depositors may be disciplining (or penalizing) their banks by shifting the
supply of uninsured deposits upward and raising the cost of bank funding.
The disciplining effect of depositors is obscured by the bank’s ability to
raise its interest rates. Thus, we need to address the three-dimensional relationship
between the price of uninsured deposits, the quantity of uninsured deposits,
and a bank’s financial health.

C. Addressing the Endogeneity of the Price Mechanism

In this section, we account for the fact that movements in
the price and quantity of uninsured deposits may be jointly determined with
the financial soundness of a bank. For this, we need to control for the possibility
that a weak bank, which is experiencing a drain of uninsured deposits due
to deteriorating fundamentals, may raise its interest rate. The idea is to
eliminate potential parameter inconsistency arising from simultaneity or reverse
causality between quantity and price movements of uninsured deposits and examine
whether a change in the interest rate can exogenously influence the quantity
of uninsured deposits.

According to the standard depositor discipline hypothesis,
a deterioration in a bank’s fundamentals should induce depositors to withdraw
their uninsured deposits from this weak bank. Graphically, this would represent
an upward shift of the supply curve of uninsured deposits, where the equilibrium
moves along the demand curve to a lower quantity and higher price of uninsured
deposits. It is possible, however, that a weak bank may raise its interest
rate on uninsured deposits further to contain the deposit drain, hence shifting
upward the demand curve for uninsured deposits. To disentangle these two effects,
we need to control for the possible price impact of deteriorating bank fundamentals
(i.e., the supply shift due to deteriorating fundamentals) and examine whether
a rise in the interest rates can exert a causal impact on the quantity of
uninsured deposits (i.e., a shift in the bank’s demand for uninsured deposits).

The way the AB model controls for the dynamic interactions
between quantity and price movements of uninsured deposits is by using internal
instruments. Appropriate instruments are highly correlated with the endogenous
variables and not correlated with the error term. Hansen (1982) has shown
that a first-difference transformation, like the one used in equation 1, allows
the use of suitably lagged levels of the endogenous variables as valid instruments.[20] More
recently, AB have identified how these lagged levels can be combined with
first-differences of the strictly exogenous variables to control for potential
biases induced by simultaneity or reversal causality between endogenous variables.[21] This technique allows us to determine
whether interest rate movements have an exogenous impact on the quantity
of uninsured depositors, independently of the endogenous impact of deteriorating
fundamentals on the price and quantity of uninsured deposits.

The first- and second-step estimates are presented in, respectively,
columns 5 and 6 of Table 3. The model specification passes the Sargan test,
suggesting that the relationship between the quantity of uninsured deposits
(as a share of total deposits) and the price of uninsured deposits is not
flawed by a simultaneity bias or omitted variables. Similarly, the model satisfies
the no second-order serial correlation requirement. Except for UninsIntMarg,
the coefficients remain broadly consistent with those found in the previous
regressions. In the case of UninsIntMarg, the coefficient is positive
and statistically significant. This result suggests that once we control for
the dynamic process determining the behavior of uninsured deposits, banks
are able to borrow additional uninsured deposits by offering higher interest
rates.

If banks are able to raise their uninsured liabilities by
offering higher interest rates, the question remains by how much should banks
have to raise their interest rates to benefit from an inflow of uninsured
deposits. Our results suggest that banks have to pay a relatively high price
to receive additional uninsured funds. According to column 5, the coefficient
of UninsIntMarg is 0.89. This means that if a bank were to increase
its interest margin on uninsured deposits by 1 percentage point (from, say,
2.2 percent to 3.2 percent), uninsured deposits would rise by 0.89 percentage
points (from, say, 10 percent to 10.89 percent). This seems to represent a
very high funding cost to the banks.

For illustration, assume a bank has $10 million uninsured
deposits, which represent 10 percent of the bank’s total deposits. For the
sake of this exercise, assume that at the beginning the bank pays no interest
rate on its deposits. Then, to raise more funds, it is willing to pay 1 percent
interest rate on its uninsured deposits. For simplicity, further assume that
the rise in interest rate induces an inflow of new uninsured deposits. In
this case, it is easy to show that uninsured deposits would increase by $9.99
million to $109.99 million and the bank would have to pay $1.099 million in
interest, or an 11 percent funding cost. The supply curve of uninsured deposits
seems to be fairly inelastic. It is likely that this high average funding
cost comes from large differences among banks of different qualities. In Section
IV.A we examine the possibility of a nonlinear relationship between the interest
elasticity and a bank’s quality.

The results suggest that uninsured depositors discipline
their banks. This is an interesting result, given that approximately 75 percent
of all deposits are guaranteed under the U.S. deposit insurance scheme. Thus,
despite a high level of deposit insurance, we find evidence that in the United
States the depositors holding the remaining 25 percent of uninsured deposits
discipline their banks.

IV. Robustness Tests

A. Weak versus Healthy Banks

In this section, we examine whether depositors in weak banks
are more sensitive and require a higher price premium than depositors in healthy
banks. We use the CAMEL ratings to subdivide our sample of banks into different
quality categories. CAMEL ratings are the result of a supervisory examination
and are confidential information. They assess the Capital adequacy, Asset
quality, Management, Earnings, and Liquidity of banks and express an overall
rating on a scale of 1 to 5 in ascending order, where 1 is the best rating
and 5 is the worst rating.[22] Although
depositors are not generally aware of changes in the CAMEL rating of their
bank, these ratings are a good proxy for the overall quality of a bank.

Given the time series nature of the econometric model, we
needed a system for categorizing the quality of a bank that was consistent
for a given bank throughout the entire sample. Thus, using the CAMEL ratings,
we have computed three broad types of banks; “good” banks (if the bank always
had a CAMEL rating of 1 or 2), “bad” banks (if the bank ever earned a CAMEL
rating of 3, 4, or 5 at any time during the sample), and “very bad” banks
(if the bank ever earned a CAMEL rating of 4 or 5 at any time during
the sample).[23] The majority of banks included in
our regression results are good banks (1,307 banks). There were also 556 bad
banks and 207 very bad banks. On average, the good banks included in our regressions
held a lower percentage of uninsured deposits to total deposits than banks
with worse CAMEL ratings. Good banks held 15.2 percent of uninsured deposits
(as a share of total deposits), whereas the bad banks had an average uninsured
deposits of 16.2 percent, and the very bad banks, those with CAMEL ratings
of 4 or 5, had an average of 16.7 percent of uninsured deposits to total deposits.[24]

Table 4 presents the regression results across different
CAMEL-based categories. Column 1 reprints the results of the benchmark model
specification, that is, the first-step version of the model with the endogenous
price mechanism presented in column 5 of Table 3. The next three columns in
Table 4 present the results of the same model specification run under the
various CAMEL-based bank categories. Column 2 presents the regression results
for the good banks, column 3 for the bad banks, and column 4 for the very
bad banks.

According to our benchmark model presented in Section III.A,
banks can raise the ratio of uninsured deposits to total deposits by increasing
their uninsured deposit interest rates, but only by paying a relatively high
price (a 1 percentage point increase in the interest margin raises the ratio
of uninsured deposits to total deposits by 0.89 percentage point). By differentiating
between different qualities of banks, we find that for a 1 percentage point
increase in the interest rate, good banks can raise a relatively higher proportion
of uninsured deposits than banks with a lower-quality standing. This is shown
in column 2, where the coefficient of UninsIntMarg is higher (1.29)
than that for the benchmark model (0.89) in column 1. We also find that weak
banks cannot attract more uninsured deposits (as a share of total deposits)
by raising their uninsured deposit interest rates. This is shown in columns
3–5, where the coefficients of UninsIntMarg are not statistically significant
for banks with a CAMEL rating worse than 2.

B. Alternative Interest Margins

In this section, we check whether our results are robust
to different interest margin specifications. So far, our price variable UninsIntMarg
reflects the spread between the interest rate on jumbo CDs and the interest
rate on total deposits in bank i. Because this variable tracks interest
rate movements between two different types of deposits held in a particular
bank, it can be interpreted as capturing the ability of a bank to change the
composition of its deposits by changing its relative deposit interest rates.

Next, we compute two further interest rate margins that capture
differences in interest rates among banks, by comparing a bank’s interest
rates with the industry average. First, we compute the margin between the
interest rate of bank i on a jumbo CD and the annual industry average
interest rate on jumbo CDs (JumboIntMarg). This price variable captures
the extent to which a particular bank can attract new uninsured deposits by
offering a higher interest rate on its uninsured deposits relative to the
industry average. Presumably, a rise in JumboIntMarg is associated
with a rise in uninsured deposits (as a share of total deposits) in bank i.

We also compute the margin between the average deposit interest
rate of bank i and the industry average deposit interest rate (DepIntMarg).
We would expect an increase in DepIntMarg, which raises the average
price of deposits in bank i, to augment total deposits. This effect
may or may not be associated with a rise in uninsured deposits (as a share
of total deposits), depending on whether the bank attracts mostly insured
or uninsured deposits.

Table 5 provides the partial correlation
between the alternative interest rate margins. The margin that a bank pays
for uninsured deposits
relative to its overall interest rate, UninsIntMarg, and the margin
it pays for uninsured deposits relative to the industry, JumboIntMarg,
are highly positively correlated both in levels and in first differences.
The interest rate margin that a bank pays on total deposits above the industry
average, DepIntMarg, is much less highly correlated to the other
interest rate measures. This would tend to indicate that changes in the UninsIntMarg
are being driven by changes in the interest rate on jumbo CDs rather than
by changes in the interest rate on deposits more generally.

The regression results are presented in Table 6. We find
that if a bank raises its average interest rates, it attracts mostly insured
deposits: the ratio of uninsured deposits to total deposits falls. This is
shown in column 3, where the coefficient of DepIntMarg is negative
and statistically significant. This result is consistent with our prior hypothesis
that deposit insurance mitigates depositor discipline. It seems to be relatively
easy for a bank to attract insured deposits by raising its interest rates.
In the case of uninsured deposits, however, depositors seem to be more cautious
before entrusting their savings to a particular bank, examining both the risk
profile of a bank and the rate of return it offers. This result is robust
across different specifications, including when the model controls for movements
in other interest margins, such as UninsIntMarg and JumboIntMarg,
as is shown in columns 5–7.

We also find that a bank cannot automatically increase its
uninsured deposits (as a share of total deposits) by raising the interest
rate on uninsured deposits above the industry average. This is shown in column
2, where the coefficient of JumboIntMarg is not statistically significant.
Again, this result is robust across specifications as shown in columns 6 and
7. The only exception is found in column 4, where the coefficient of JumboIntMarg
is strongly significant and negative, in conjunction with that of UninsIntMarg,
which is strongly significant and positive. This result needs to be interpreted
with a high degree of caution since it is severely tainted by the high degree
of correlation between the two interest rates (see Table 5).

Overall, our results suggest that a bank can raise its share
of uninsured deposits to total deposits by raising the premium it pays on
uninsured deposits relative to the interest rate on total deposits (the coefficient
of UninsIntMarg is positive). However, the evidence that a bank can
induce an inflow of (new) uninsured deposits (as a share of total deposits)
by raising its uninsured deposit interest rate above the industry average
is much weaker (the coefficient of JumboIntMarg is not statistically
significant).

Finally, if a bank raises its deposit interest rates above
the industry average, it attracts mostly insured deposits (the coefficient
of DepIntMarg is negative), raising total deposits (the denominator
of the dependent variable). Note, however, that the very strong negative coefficient
of DepIntMarg may suggest that a large part of the movement in the
ratio of uninsured deposits to total deposits is related to movements in total
deposits. In fact, it is possible to argue that weak banks may actually reduce
the interest rate premium on uninsured deposits rather than raise it, as suggested
earlier. This would still be consistent with the positive coefficient of UninsIntMarg
but would suggest that banks in need of funding may try to substitute insured
deposits for uninsured deposits.[25]

V. Conclusions

This paper attempts to capture banks’ dynamic response to
depositors’ desire to withdraw their uninsured deposits if the performance
of their bank is no longer satisfactory. In our view, depositors’ reaction
and banks’ response must be modeled as a jointly determined process. When
a bank’s fundamentals are deteriorating, both the price and quantity of uninsured
deposits may be shifting simultaneously, obscuring the effect of depositor
discipline.

According to our results, the behavior of uninsured deposits
is sensitive to bank fundamentals. This result, which is robust over a broad
selection of banks and thrifts, shows evidence that the quality of a bank
helps determine the quantity of uninsured deposits that a particular bank
can attract for a given price. Furthermore, we find that the behavior of uninsured
deposits is driven not only by changes in bank quality but also by price movements.
Once we control for the endogeneity between the price and quantity mechanisms
of uninsured deposits, our results suggest that banks are able to increase
the quantity of uninsured deposits by raising the price (i.e., the interest
rate) of uninsured deposits. We find, however, that they are able to do so
only at a relatively high price. When differentiating among various qualities
of banks, we find that good banks are in a better position to use prices to
attract uninsured deposits than banks with a lower quality standing, suggesting
that the price of raising uninsured deposits is related to a bank’s quality,
which is consistent with our hypothesis of depositor discipline.

This paper confirms the presence of depositor discipline
in the U.S. banking system. Uninsured depositors monitor the health of their
banks and discipline bad bank behavior by withdrawing their uninsured deposits
and demanding a higher risk premium. Given the high level of U.S. deposit
insurance coverage, this discipline for practical purposes affects only 25 percent
of all deposits. Nevertheless, our results suggest that regulators may be
able to exploit the information contained in declining insured deposits or
a rising premium for uninsured deposits. In addition, if regulators can encourage
depositor discipline, our results suggest that depositors would generate a
greater penalty to banks for excessive risk appetite.

Board of Governors of the Federal Reserve System and the
U.S. Department of the Treasury, 2000, “The Feasibility and Desirability of
Mandatory Subordinated Debt,” Report to the Congress (December), Washington,
DC.

U.S. Department of the Treasury, 1991, “Modernizing the Financial
System: Recommendations for Safer, More Competitive Banks” (Washington, DC:
Department of the Treasury).

[1] In the United States., the 1986–95 savings and loan (S&L)
crisis resulted in the closure of approximately 50 percent of all federally
insured thrift institutions (Curry and Shibut, 2002). Outside the United States,
since 1980 a number of systemically important countries, such as Canada, Sweden,
Switzerland, and Japan, have been confronted with major problems involving
significant numbers of bank failures (Bartholomew and Gup, 1999).

[2] The budgetary cost of the 1997 Thai and Korean banking
crises are estimated to have reached 30 percent of GDP, while the cost of
the Indonesian banking crisis in the same year is believed to have reached
up to 50 percent of GDP (Caprio, 2001).

[3] In the third consultative paper of the New Basel Accord,
the Basel Committee on Banking Supervision (2003) has designated market discipline
as part of the third pillar of a sound financial architecture, after minimum
capital requirements and supervisory review process.

[4] The U.S. banking crisis of the early 1930s showed the
magnitude and degree of danger posed by the phenomenon of widespread bank
runs.

[5] Demirgüç-Kunt and Sobaci (2001) provide a data set on
the existence and extent of deposit insurance schemes across countries. For
general reviews of deposit insurance systems around the world see also Demigürç-Kunt
and Kane (2002) and Garcia (2000).

[6] Some people have argued that the U.S. deposit insurance
plan is responsible for the relatively small number of bank failures that
has occurred since the creation of the FDIC (see discussion in FDIC, 1998).
See also U.S. Department of the
Treasury (1991) and U.S. Congressional Budget Office (1991). Kane (1992) provides
evidence of the role of deposit insurance in the S & L crisis, and Kaufman
(1995) discusses the impact of deposit insurance on bank capital.

[7] In a related paper, Demirgüç-Kunt and Huizinga (1999)
find that banks’ funding costs tend to be lower and less sensitive to fluctuations
in bank fundamentals in countries with explicit deposit insurance.

[8] Total deposits include domestic deposits (insured and
uninsured) and foreign deposits if applicable. These data are published in
the “Quarterly Condition and Income Reports,” generally knows as “Call Reports.”
The estimate for uninsured deposits is taken from the banks’ self-reported
data published in the same source.

[9] These banks tend to be primarily commercial banks and,
to a lesser extent, consumer banks and trust companies.

[10] A good overview
of this strand of literature is given in Board of Governors of the Federal
Reserve System (1999) and Board of Governors of the Federal Reserve System
and the U.S. Treasury (2000), and U.S. empirical evidence on private investors’
abilities to assess the financial condition of banks can be found in Flannery
(1998).

[11] Typically, in the literature on depositor discipline,
risk measures are based on CAMEL indicators derived from balance sheets and
income statements, such as non-accrual loans, past-due loans, other real estate
ownership, equity ratios, leverage ratios, measure of profits, and loss ratios.

[12] Using data for Argentina, Chile, and Mexico, Martinez
Peria and Schmukler (2001) find that even small insured depositors exert depositor
discipline by withdrawing deposits from weak banks. Barajas and Steiner (2000)
notice that banks with stronger fundamentals benefit from lower interest costs
and higher lending rates in Colombia. Meanwhile, Birchler and Maechler (2002)
discover that in Switzerland depositors respond to institutional changes in
the Swiss depositor protection system and behave differently across different
types of banks with different implicit insurance levels.

[13]For further readings, see also Arellano and Bover (1995)
and Blundell and Bond (1998).

[14] The residential loans in this variable include only
mortgages made on properties that house one to four families.

[15] In addition, controlling for both the inflation rate
and a proxy for the nominal interest rate allows us to talk about movements
in the real interest rate.

[16] We have run the model specification of column 1 with
a large number of alternative variables and sets of variables, all of which
turned out either not to be statistically significant or not to add value
to the interpretation of the results.

[17] The last two results suggest that the share of uninsured deposits to total deposits falls when real interest rates rise.

[18] In the AB framework, by construction there should be
first-order autocorrelation in the residuals, but there should be no second-order
autocorrelation.html Our findings are consistent with this requirement. The null
hypothesis of no first-order autocorrelation in the differenced residuals
is rejected, but it is not possible to reject the null hypothesis of no second-order
autocorrelation.

[19] Under the Sargan test, the null hypothesis assumes that
the over-identifying restrictions are valid. If the Sargan test from the one-step
homoskedastic estimator rejects the null hypothesis, this may be due to heteroskedasticity
(the AB framework allows for the presence of first-order autocorrelation in
the differenced residuals, as this type of autocorrelation does not imply
inconsistent estimates). Using a two-step estimator can produce large efficiency
gains. For this reason, the two-step Sargan test is typically used for inference
on model specification.

[20] The use of lagged observations of the independent and
explanatory variables as instruments was first introduced in the GMM framework
pioneered by Hansen (1982). Efficient GMM estimators will typically exploit
a different number of instruments in each time period. For more details, see
Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond
(1998).

[21] This methodology has been used widely in the growth
literature, where it is important to disentangle the factors that contribute
to economic growth (such as financial development or capital accumulation)
from the reverse effect of how economic growth influences these factors. A
good illustration is presented in Levine, Loayza and Beck (2000).

[22] In 1997, a fifth component, Sensitivity was added to
the rating system to capture sensitivity to market risk. Since then, the acronym
has been changed to CAMELS.

[23] Banks may switch from one rating category to another,
and for the purpose of this exercise it is not relevant to restrict our analysis
to banks that always remain within one particular category. Instead, we have
computed broader categories that allow a bank’s performance to be followed
across CAMEL ratings.

[24] This result is driven mostly by the large sample size
differences across categories and the relatively flat distribution curves.
Typically, the sample size problem (especially in the case of bad and very
bad banks) is best addressed by looking at the median rather than the mean.
Another important note is that a bank needs to have only a one-time CAMEL
3 or worse rating to be categorized as a “bad” bank. This factor helps explain
the relatively high share of uninsured deposits for our category of bad and
very bad banks. If we restrict our attention to the CAMEL rating categories,
the median share of uninsured deposits to total deposits is highest for the
best-rated banks (12.3 percent) and drops progressively to 8.5 percent for
the worst-rated banks.

[25] Further preliminary research suggests that a rise in
the interest rate on uninsured deposits still has a substantial effect on
the quantity of uninsured deposits as a share of total assets. These results,
which eliminate the possible denominator effect, are consistent with those
presented in this paper.